Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph q in a data graph G. An important branch of matching algorithms exploit the backtracking search approach which recursively extends intermediate results following a matching order of query vertices. It has been shown that the matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In recent years, many advanced techniques for query vertex ordering (i.e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms. Instead of using the fixed heuristics to generate the matching order, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduces the number of redundant enumerations. With the help of the reinforcement learning framework, our model is able to consider the long-term benefits rather than only consider the local information at current ordering step.Extensive experiments on six real-life data graphs demonstrate that our proposed matching order generation technique could reduce up to two orders of magnitude of query processing time compared to the state-of-the-art algorithms.

翻译：Subgraph 匹配是使用图形结构化数据的不同字段中的一个基本问题。 Subgraph 匹配算法在数据图形 G. 匹配算法的一个重要分支, 利用在查询脊椎相匹配的顺序后反复扩展中间结果的回溯跟踪搜索方法。 已经显示匹配顺序在使用图形结构化数据的不同字段中起着关键作用。 最近几年, 提出了许多查询脊椎排序( 即匹配顺序生成) 的先进技术, 以根据预设的超常规则, 减少无法预测的中间结果。 匹配算法的一个重要分支, 在本文中, 我们首次应用“ 强化学习( RL) ” 和“ 图形神经网络( GNNS) ” 技术, 以生成基于次跟踪的子跟踪匹配算法的时间效率高。 我们的模型可以捕捉并充分利用图形信息, 从而确定基于适应性学习模型的状态的垂直排列顺序, 以大幅降低基于超常年度规则的中间结果。 在本文中我们第一次应用“ 强化” 和“ 图表生成” 的流程中, 仅考虑“ 学习“ 真正的流程” 真正的流程”, 以学习“ 以显示“ ” 真正的流程” 。